ArticleGenome biology2021
Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
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- Transcriptomic Analysis Reveals PACS1 as a Potential Shared Candidate Biomarker for Apical Periodontitis and Osteoporosis.International journal of molecular sciences · 2026Article
- Metabolic reprogramming-associated genomic instability drives colorectal cancer progression via the UBXN1-NF-κB axis.American journal of translational research · 2026Article
- LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.PloS one · 2026Article
- High throughput identification of genetic regulators of microglial inflammatory processes in Alzheimer's disease.Journal of neuroinflammation · 2025Article
- Benchmarking large-scale single-cell RNA-seq analysis.bioRxiv : the preprint server for biology · 2025Article
- Warp analysis research pipelines: cloud-optimized workflows for biological data processing and reproducible analysis.Bioinformatics (Oxford, England) · 2025Article
- The current landscape and emerging challenges of benchmarking single-cell methods.Briefings in bioinformatics · 2025Review
- Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons.Nature communications · 2025Article
- Human induced neural progenitor cells generated from three-dimensional aggregate-based culture significantly improve post-stroke recovery in tMCAO mice.Stem cell research & therapy · 2025Article
- Emerging technologies of single-cell multi-omics.Haematologica · 2025Review
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- The impact of PCR duplication on RNAseq data generated using NovaSeq 6000, NovaSeq X, AVITI, and G4 sequencers.Genome biology · 2025Article
- Article
- Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons.bioRxiv : the preprint server for biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Integrating spatial omics with routine haematoxylin and eosin in formalin-fixed paraffin-embedded: a step-by-step clinical workflow.F1000Research · 2025Review
- Seurat function argument values in scRNA-seq data analysis: potential pitfalls and refinements for biological interpretation.Frontiers in bioinformatics · 2025Article
- Uncovering functional lncRNAs by scRNA-seq with ELATUS.Nature communications · 2024Article
- Scywalker: scalable end-to-end data analysis workflow for long-read single-cell transcriptome sequencing.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied.
resultsHere, we systematically benchmark the performance of 10 end-to-end preprocessing workflows (Cell Ranger, Optimus, salmon alevin, alevin-fry, kallisto bustools, dropSeqPipe, scPipe, zUMIs, celseq2, and scruff) using datasets yielding different biological complexity levels generated by CEL-Seq2 and 10x Chromium platforms. We compare these workflows in terms of their quantification properties directly and their impact on normalization and clustering by evaluating the performance of different method combinations. While the scRNA-seq preprocessing workflows compared vary in their detection and quantification of genes across datasets, after downstream analysis with performant normalization and clustering methods, almost all combinations produce clustering results that agree well with the known cell type labels that provided the ground truth in our analysis.
conclusionsIn summary, the choice of preprocessing method was found to be less important than other steps in the scRNA-seq analysis process. Our study comprehensively compares common scRNA-seq preprocessing workflows and summarizes their characteristics to guide workflow users.
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